Photovoltaic (PV) systems are widely used as standalone off-grid power systems, as sources of supplemental electricity, such as for use in a building or house, and in power grid-connected systems. A power grid is an electricity generation, transmission, and distribution infrastructure that delivers electricity from suppliers to consumers. Typically, when integrated into a power grid, photovoltaic systems are collectively operated as a photovoltaic fleet. As electricity is consumed almost immediately upon production, planners and operators of power grids need to be able to accurately gauge both on-going and forecasted power generation and consumption.
Photovoltaic fleets participating in a power grid are expected to exhibit predictable power generation behaviors, and production data is needed at all levels of the power grid. Accurate production data is particularly crucial when a photovoltaic fleet makes a significant contribution to a power grid's overall energy mix. Photovoltaic production forecasting involves obtaining a prediction of solar irradiance (or measured solar irradiance, where production is being simulated) that is combined with ambient temperature data and each photovoltaic plant's system specification in a simulation model, which then generates a forecast of individual plant energy output production. The individual photovoltaic plant forecasts can then be combined into a photovoltaic fleet energy forecast, such as described in commonly-assigned U.S. Pat. Nos. 8,165,811; 8,165,812; 8,165,813, all issued to Hoff on Apr. 24, 2012; U.S. Pat. Nos. 8,326,535; 8,326,536, issued to Hoff on Dec. 4, 2012; and U.S. Pat. No. 8,335,649, issued to Hoff on Dec. 18, 2012, the disclosures of which are incorporated by reference.
A grid-connected photovoltaic fleet can be operationally dispersed or concentrated in one site. The aggregate contribution of a photovoltaic fleet to a power grid can be determined as a function of the individual photovoltaic system contributions. Photovoltaic system specifications are crucial to forecasting plant power output and include geographic location, PV rating, inverter rating, tilt angle, azimuth angle, other losses, obstruction profile (elevation angles in multiple azimuth directions), and other factors.
Inaccuracies in system specifications can adversely affect forecasting. Accepting user-supplied system specifications has the advantage of simplicity, albeit at the risk of being inaccurate. Alternatively, system specifications can be inferred from measured photovoltaic production data, which is often available. For instance, in utility-scale operations, photovoltaic plant owners instrument plant components for grid operations and market settlement. Similarly, third party photovoltaic leasing companies, who are responsible for maintaining their leased systems, outfit the systems with recording and telemetry systems to report production data. The production data is also evaluated to determine when maintenance is needed; personnel are typically dispatched upon a significant divergence between measured and simulated performance data. Finally, photovoltaic systems often have a guaranteed production level. Performance monitoring systems collect measured performance data to substantiate guaranteed production claims by comparing simulation results over a long period.
Inferred system specifications from measured photovoltaic production data can be daunting, particularly from a computational volume perspective in which the number of possible combinations of system specification parameters is not limited. Consider a simple example. Suppose that the goal is to infer all photovoltaic system specifications within 1°, where no orientation information is known about the photovoltaic system, and to model obstructions as opaque features in seven different 30° azimuthal bins, that is, 75° to 105°, 105° to 135°, . . . , 255° to 285°). A brute force approach requires testing all possible combinations in a combinatorics search space. Azimuth may be between 90° and 270° (180 possibilities). Tilt angle may be between 0° and 90° (90 possibilities). The obstruction's elevation angle for each azimuth bin may be between 0° and 50° (50 possibilities). These parameters are representative of most kinds of obstructions. By combining these parameters together, the brute force approach would require simulations for 12 quadrillion candidate systems, which does not even include variations in inverter size, loss factor, and other parameters. The complexity of the photovoltaic simulations required in determining system specifications further compounds the issue because these simulations can be computationally costly.
Photovoltaic system output is particularly sensitive to shading due to cloud cover. A photovoltaic array with only a small portion covered in shade can suffer a dramatic decrease in power output. For a single photovoltaic system, power capacity is measured by the maximum power output determined under standard test conditions and is expressed in units of Watt peak (Wp). The actual power could vary from the rated system power capacity depending upon geographic location, time of day, weather conditions, and other factors. Moreover, photovoltaic fleets scattered over a large area are subject to different location-specific cloud conditions with a consequential effect on aggregate power output.
Consequently, photovoltaic fleets operating under cloudy conditions can exhibit variable and unpredictable performance. Conventionally, fleet variability is determined by centrally collecting direct power measurements from individual systems or equivalent indirectly-derived power measurements. To be of optimal usefulness, the direct power measurement data must be collected in near-real time at fine-grained time intervals to generate high resolution power output time series. The practicality of this approach diminishes as the number of systems, variations in system specifications, and geographic dispersion grow. Moreover, the costs and feasibility of providing remote power measurement data can make high speed data collection and analysis insurmountable due to the transmission bandwidth and storage space needed coupled with the processing resources required to scale quantitative power measurement analysis upwards as fleet size grows.
For instance, one direct approach to obtaining high speed time series power production data from a fleet of existing photovoltaic systems is to install physical meters on every system, record the electrical power output at a desired time interval, such as every 10 seconds, and sum the recorded output across all systems in the fleet. The totalized power data could then be used to calculate the time-averaged fleet power, variance, and similar values for the rate of change of fleet power. An equivalent direct approach for a future photovoltaic fleet or an existing photovoltaic fleet with incomplete metering and telemetry is to collect solar irradiance data from a dense network of weather monitoring stations covering all anticipated locations of interest at the desired time interval, use a photovoltaic performance model to simulate the high speed time series output data for each system individually, and sum the results at each time interval.
With either direct approach to obtaining high speed time series power production data, several difficulties arise. First, in terms of physical plant, calibrating, installing, operating, and maintaining meters and weather stations is expensive and detracts from cost savings otherwise afforded through a renewable energy source. Similarly, collecting, validating, transmitting, and storing high speed data for every photovoltaic system or location requires collateral data communications and processing infrastructure at significant expense. Moreover, data loss occurs whenever instrumentation or data communications fail.
Second, in terms of inherent limitations, both direct approaches only work for times, locations, and photovoltaic system configurations when and where meters are pre-installed. Both direct approaches also cannot be used to directly forecast future system performance since meters must be physically present at the time and location of interest. Also, data must be recorded at the time resolution that corresponds to the desired output time resolution. While low time-resolution results can be calculated from high resolution data, the opposite calculation is not possible. For example, photovoltaic fleet behavior with a 10-second resolution cannot be determined from data collected with a 15-minute resolution.
The few solar data networks that exist in the United States, such as the ARM network, described in G. M. Stokes et al., “The atmospheric radiation measurement (ARM) program: programmatic background and design of the cloud and radiation test bed,” Bulletin of Am. Meteor. Soc., Vol. 75, pp. 1201-1221 (1994), the disclosure of which is incorporated by reference, and the SURFRAD network, do not have high density networks (the closest pair of stations in the ARM network are 50 km apart) nor have they been collecting data at a fast rate (the fastest rate is 20 seconds in the ARM network and one minute in the SURFRAD network). The limitations of the direct measurement approaches have prompted researchers to evaluate other alternatives. Researchers have installed dense networks of solar monitoring devices in a few limited locations, such as described in S. Kuszamaul et al., “Lanai High-Density Irradiance Sensor Network for Characterizing Solar Resource Variability of MW-Scale PV System.” 35th Photovoltaic Specialists Conf., Honolulu, Hi. (Jun. 20-25, 2010), and R. George, “Estimating Ramp Rates for Large PV Systems Using a Dense Array of Measured Solar Radiation Data,” Am. Solar Energy Society Annual Conf. Procs., Raleigh, N.C. (May 18, 2011), the disclosures of which are incorporated by reference. As data are being collected, the researchers examine the data to determine if there are underlying models that can translate results from these devices to photovoltaic fleet production at a much broader area, yet fail to provide translation of the data. In addition, half-hour or hourly satellite irradiance data for specific locations and time periods of interest have been combined with randomly selected high speed data from a limited number of ground-based weather stations, such as described in CAISO 2011. “Summary of Preliminary Results of 33% Renewable Integration Study—2010,” Cal. Public Util. Comm. LTPP No. R.10-05-006 (Apr. 29, 2011) and J. Stein, “Simulation of 1-Minute Power Output from Utility-Scale Photovoltaic Generation Systems,” Am. Solar Energy Society Annual Conf. Procs., Raleigh, N.C. (May 18, 2011), the disclosures of which are incorporated by reference. This approach, however, does not produce time synchronized photovoltaic fleet variability for any particular time period because the locations of the ground-based weather stations differ from the actual locations of the fleet. While such results may be useful as input data to photovoltaic simulation models for purpose of performing high penetration photovoltaic studies, they are not designed to produce data that could be used in grid operational tools.
Similarly, accurate photovoltaic system specifications are as important to photovoltaic power output forecasting as obtaining a reliable solar irradiance forecasts. Specifications provided by the owner or operator can vary in terms of completeness, quality and correctness, which can skew power output forecasting. Moreover, in some situations, system specifications may simply not be available, as can happen with privately-owned systems. Residential systems, for example, are typically not controlled or accessible by power grid operators and other personnel who need to understand and gauge expected photovoltaic power output capabilities and shortcomings and even large utility-connected systems may have specifications that are not publicly available due to privacy or security reasons.
Therefore, a need remains for an approach to determining photovoltaic system configuration specifications, even when configuration data is incomplete or unavailable, and as few times as possible during the searching process, for use in forecasting photovoltaic energy production output.